>> Narrator: Live from Stanford University, it's theCUBE! Covering Stanford Women in Data Science 2020, brought to you by SiliconAngle Media. >> Hi, and welcome to the cube, I'm your host, Sonia Tagare. And we're live at Stanford University, covering the fifth annual WiDS, Women in Data Science Conference. Joining us today is Ya XU, the head of data science at LinkedIn. Ya Welcome to the cube. >> Thank you for having me. >> So tell us a little bit about your role and about LinkedIn. >> So LinkedIn is, first of all, the biggest professional social network, where we have a massive economic graph that we have been creating with millions actually close to 700 million members and millions of companies and jobs and of course, you know, with students of skills and also schools as well as part of it. And, and I lead the data science team at LinkedIn. And my team really spans across the global presence that LinkedIn offices have. And yeah really working on various different areas. That's both thinking about how we can iterate and understand and improve our products, that we deliver to our members and our customers. And also at the same time thinking about how we can make our infrast6ructure more efficient, and thinking about how we can make our sales and marketing more efficient as well, so we really span across. >> And how has the use of data science evolved to deliver a better user experience for users of LinkedIn? >> Yeah, so first of all, I think we LinkedIn in general, we truly believe that everybody can benefit from better data, better data access, in general. So we're certainly using data to continuously understand better of what our members are looking for. As a simple example, is that whenever we launch new feature, we're not just  blindly deciding ourselves what is the better feature for our members, but we actually understand how our users are reacting to it. Right? So we use data to understand that, and then certainly making decisions, and whether we should be eventually launching this feature to all members or not. So that's a very prominent way for us to use data. And obviously, we also use data to understand and just even before we build certain features. Is this sort of feature that's right feature to build. We do both survey and understand the survey data, but also at the same time understanding just user behavior data for us to be able to come up with better features for users. >> And do you use AB testing as well? >> Oh absolutely, Yeah. So we do a lot of AV experiments. That's what, I was not trying to use that word by that like that terminology, but this is what we use to have an understanding of user features that we are developing, that we are putting in front of our users. Is that what they enjoy as much as we think they will enjoy? >> Right, so you had a talk today about creating global economic opportunities with responsible data. So give us some highlights from your talk. >> So, first of all, at LinkedIn we we truly believe in the vision that we are working towards, which is really creating economic opportunity for every member of the global workforce. And if you're kind of starting from that, and thinking about that is our sort of the axiom that we're working towards, and then thinking about how you can do that, and obviously, the sort of the table stake or just the fundamental thing that we have to start with is to be able to preserve the privacy of our members as we are leveraging the data that our members entrust with us. Right, so how can we do that? We have some early effort in using and developing differential privacy as a technique for us to do a lot better. Always regarding preserving their privacy as we're leveraging the data, but also at the same time, it doesn't ends there, right? Because you're thinking about creating opportunity. It's not just about to preserve their privacy, but also, when we are leveraging the data, how can we leverage the data in a way that is able to create opportunity in a fair way? So here is also a lot of effort that we're having with regarding, how can we do that? And what does fairest mean? What are the ways we can actually turn some of the key concepts that we have into action that is really able to drive the way we develop product, the way that we think about responsible design, and the way that we build our algorithms, the way that we measure in every single dimension. >> And and speaking about that bias, at the opening address, they mentioned that diversity is really great because it provides many perspectives, and also helps reduce this bias. So how have you at LinkedIn been able to create a more diverse team? >> So first of all, I think it's certain we all believe that diversity is certainly better as we building product. Thinking about if you have a diverse team that is really a representation of the customer and some members that you're serving, then definitely you're able to come up with better features that is able to serve the needs of the population of our members. But also at the same time, that's just the right thing to do as well. Right, thinking about we all have had experiences we may not you know, feel as much belonging when we walk into a room that we are the only person that we identify with to be in that room. And, we certainly wanted to be able to create that environment for all the employees as well. And and thinking about, I think there is also studies that has done as what makes a high performing team. Some of the studies has done I google with the psychological safety aspects of it, which is really there's a lot of brain science that says when you make people feel they belong, that they will actually be so much more creative and innovative and everything right. So we have that belief. But tactically, there are many things that we're doing from all the divs aspect, right? How can you bring diversity, inclusion and belonging? Starting from and hiring, right? So we certainly are very much emphasized how can we increase the diversity of individuals that we're bringing to LinkedIn? And when they are at LinkedIn, can we make them feel more belonging, and feel more included in every aspects? We have different inclusion groups, right? We have I mean, obviously, I'm very much involved in Women tech. At LinkedIn we have both money efforts that we do to help women at LinkedIn in engineering, and in other groups as well to feel they belong to this community. At the same time, there is concrete actions that we're taking too. Right, that we are helping women to have a much better understanding, and aware of some of the ways that we operate that is slightly different from maybe our male colleagues will operate, right? There are certain things that we're doing to change the current processes, hiring processes, promotion process, that we are able to bring more equal footing to the way that we're thinking about gender gap and gender diversity. >> Right, that's great. And what advice would you give to women who are just starting college or who are just out of college who are interested in going into data science. >> So I want to say the biggest learning for me, is just have that can do attitude. I, you know, the woman biologically and all just like in every way, we're not any less than men. And that you certainly have seen many strong and very talented women that we have in the field. So don't let people's perceptions or biases around you to bring you down. And then thinking about what you wanted, and then just go for it, and then go for the the advice that you can get from people. And then there are so many as you can see in the conference today, so many talented women that you can reach out to who are winning and very willing to help you as well. >> And in this age of AI and ML, where do you see data science going in the future? >> That's a really interesting question. So in the way that, you know, data science I want to say is a field that is really broad, right? So if you're thinking about things that I would consider to be part of data science may not necessarily part of AI, but some of the course of influence that is extremely popular and important. And then I think the fields will continue to evolve, there are going to be and then the fields are continually overlapping with each other as well. You cannot do data science without understanding or have a strong skill in AI and machine learning. And you also can't do great machine learning without understanding the data science either. Right? So thinking about some of the talk that definitely colder earlier was sharing, as in you know, you can blind in the wrong algorithm and without realizing the bias. That all the algorithm is really just detecting the machines that's using the images versus you know, actually detecting the difference between broken bones or not right, like so. So I think having, I do see there is a continuously big overlap and I think the individuals who are involved in both communities should continue to be very comfortable being in that way too. >> Right, great. Thank you so much for being on theCUBE and thank you for your insight. >> Of course, thank you for having me. >> I'm your host, Sonia Takari. Thank you for watching theCUBE and stay tuned for more. (Upbeat music)